# !pip install git+https://github.com/alberanid/imdbpy
# !pip install pandas
# !pip install numpy
# !pip install matplotlib
# !pip install seaborn
# !pip install pandas_profiling --upgrade
# !pip install plotly
# !pip install wordcloud
# !pip install Flask
# Import Dataset
# Import File from Loacal Drive
# from google.colab import files
# data_to_load = files.upload()
# from google.colab import drive
# drive.mount('/content/drive')
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
import collections
import plotly.express as px
import plotly.graph_objects as go
import nltk
import re
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
from nltk.probability import FreqDist
from nltk.util import ngrams
from plotly.subplots import make_subplots
from plotly.offline import iplot, init_notebook_mode
from wordcloud import WordCloud, STOPWORDS
from pandas_profiling import ProfileReport
%matplotlib inline
warnings.filterwarnings("ignore")
nltk.download('all')
[nltk_data] Downloading collection 'all' [nltk_data] | [nltk_data] | Downloading package abc to [nltk_data] | C:\Users\pawan\AppData\Roaming\nltk_data... [nltk_data] | Package abc is already up-to-date! [nltk_data] | Downloading package alpino to [nltk_data] | C:\Users\pawan\AppData\Roaming\nltk_data... [nltk_data] | Package alpino is already up-to-date! [nltk_data] | Downloading package biocreative_ppi to [nltk_data] | C:\Users\pawan\AppData\Roaming\nltk_data... [nltk_data] | Package biocreative_ppi is already up-to-date! [nltk_data] | Downloading package brown to [nltk_data] | C:\Users\pawan\AppData\Roaming\nltk_data... [nltk_data] | Package brown is already up-to-date! [nltk_data] | Downloading package brown_tei to [nltk_data] | C:\Users\pawan\AppData\Roaming\nltk_data... [nltk_data] | Package brown_tei is already up-to-date! [nltk_data] | Downloading package cess_cat to [nltk_data] | C:\Users\pawan\AppData\Roaming\nltk_data... [nltk_data] | Package cess_cat is already up-to-date! 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[nltk_data] | [nltk_data] Done downloading collection all
True
# path = '/content/drive/MyDrive/Files/'
path = 'C:\\Users\\pawan\\OneDrive\\Desktop\\ott\\Data\\'
df_movies = pd.read_csv(path + 'ottmovies.csv')
df_movies.head()
| ID | Title | Year | Age | IMDb | Rotten Tomatoes | Directors | Cast | Genres | Country | Language | Plotline | Runtime | Kind | Seasons | Netflix | Hulu | Prime Video | Disney+ | Type | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | Inception | 2010 | 13+ | 8.8 | 87% | Christopher Nolan | Leonardo DiCaprio,Joseph Gordon-Levitt,Elliot ... | Action,Adventure,Sci-Fi,Thriller | United States,United Kingdom | English,Japanese,French | Dom Cobb is a skilled thief, the absolute best... | 148.0 | movie | NaN | 1 | 0 | 0 | 0 | 0 |
| 1 | 2 | The Matrix | 1999 | 16+ | 8.7 | 88% | Lana Wachowski,Lilly Wachowski | Keanu Reeves,Laurence Fishburne,Carrie-Anne Mo... | Action,Sci-Fi | United States | English | Thomas A. Anderson is a man living two lives. ... | 136.0 | movie | NaN | 1 | 0 | 0 | 0 | 0 |
| 2 | 3 | Avengers: Infinity War | 2018 | 13+ | 8.4 | 85% | Anthony Russo,Joe Russo | Robert Downey Jr.,Chris Hemsworth,Mark Ruffalo... | Action,Adventure,Sci-Fi | United States | English | As the Avengers and their allies have continue... | 149.0 | movie | NaN | 1 | 0 | 0 | 0 | 0 |
| 3 | 4 | Back to the Future | 1985 | 7+ | 8.5 | 96% | Robert Zemeckis | Michael J. Fox,Christopher Lloyd,Lea Thompson,... | Adventure,Comedy,Sci-Fi | United States | English | Marty McFly, a typical American teenager of th... | 116.0 | movie | NaN | 1 | 0 | 0 | 0 | 0 |
| 4 | 5 | The Good, the Bad and the Ugly | 1966 | 16+ | 8.8 | 97% | Sergio Leone | Eli Wallach,Clint Eastwood,Lee Van Cleef,Aldo ... | Western | Italy,Spain,West Germany,United States | Italian | Blondie (The Good) (Clint Eastwood) is a profe... | 161.0 | movie | NaN | 1 | 0 | 1 | 0 | 0 |
# profile = ProfileReport(df_movies)
# profile
def data_investigate(df):
print('No of Rows : ', df.shape[0])
print('No of Coloums : ', df.shape[1])
print('**'*25)
print('Colums Names : \n', df.columns)
print('**'*25)
print('Datatype of Columns : \n', df.dtypes)
print('**'*25)
print('Missing Values : ')
c = df.isnull().sum()
c = c[c > 0]
print(c)
print('**'*25)
print('Missing vaules %age wise :\n')
print((100*(df.isnull().sum()/len(df.index))))
print('**'*25)
print('Pictorial Representation : ')
plt.figure(figsize = (10, 10))
sns.heatmap(df.isnull(), yticklabels = False, cbar = False)
plt.show()
data_investigate(df_movies)
No of Rows : 16923
No of Coloums : 20
**************************************************
Colums Names :
Index(['ID', 'Title', 'Year', 'Age', 'IMDb', 'Rotten Tomatoes', 'Directors',
'Cast', 'Genres', 'Country', 'Language', 'Plotline', 'Runtime', 'Kind',
'Seasons', 'Netflix', 'Hulu', 'Prime Video', 'Disney+', 'Type'],
dtype='object')
**************************************************
Datatype of Columns :
ID int64
Title object
Year int64
Age object
IMDb float64
Rotten Tomatoes object
Directors object
Cast object
Genres object
Country object
Language object
Plotline object
Runtime float64
Kind object
Seasons float64
Netflix int64
Hulu int64
Prime Video int64
Disney+ int64
Type int64
dtype: object
**************************************************
Missing Values :
Age 8457
IMDb 328
Rotten Tomatoes 10437
Directors 357
Cast 648
Genres 234
Country 303
Language 437
Plotline 4958
Runtime 382
Seasons 16923
dtype: int64
**************************************************
Missing vaules %age wise :
ID 0.000000
Title 0.000000
Year 0.000000
Age 49.973409
IMDb 1.938191
Rotten Tomatoes 61.673462
Directors 2.109555
Cast 3.829108
Genres 1.382734
Country 1.790463
Language 2.582284
Plotline 29.297406
Runtime 2.257283
Kind 0.000000
Seasons 100.000000
Netflix 0.000000
Hulu 0.000000
Prime Video 0.000000
Disney+ 0.000000
Type 0.000000
dtype: float64
**************************************************
Pictorial Representation :
# ID
# df_movies = df_movies.drop(['ID'], axis = 1)
# Age
df_movies.loc[df_movies['Age'].isnull() & df_movies['Disney+'] == 1, "Age"] = '13'
# df_movies.fillna({'Age' : 18}, inplace = True)
df_movies.fillna({'Age' : 'NR'}, inplace = True)
df_movies['Age'].replace({'all': '0'}, inplace = True)
df_movies['Age'].replace({'7+': '7'}, inplace = True)
df_movies['Age'].replace({'13+': '13'}, inplace = True)
df_movies['Age'].replace({'16+': '16'}, inplace = True)
df_movies['Age'].replace({'18+': '18'}, inplace = True)
# df_movies['Age'] = df_movies['Age'].astype(int)
# IMDb
# df_movies.fillna({'IMDb' : df_movies['IMDb'].mean()}, inplace = True)
# df_movies.fillna({'IMDb' : df_movies['IMDb'].median()}, inplace = True)
df_movies.fillna({'IMDb' : "NA"}, inplace = True)
# Rotten Tomatoes
df_movies['Rotten Tomatoes'] = df_movies['Rotten Tomatoes'][df_movies['Rotten Tomatoes'].notnull()].str.replace('%', '').astype(int)
# df_movies['Rotten Tomatoes'] = df_movies['Rotten Tomatoes'][df_movies['Rotten Tomatoes'].notnull()].astype(int)
# df_movies.fillna({'Rotten Tomatoes' : df_movies['Rotten Tomatoes'].mean()}, inplace = True)
# df_movies.fillna({'Rotten Tomatoes' : df_movies['Rotten Tomatoes'].median()}, inplace = True)
# df_movies['Rotten Tomatoes'] = df_movies['Rotten Tomatoes'].astype(int)
df_movies.fillna({'Rotten Tomatoes' : "NA"}, inplace = True)
# Directors
# df_movies = df_movies.drop(['Directors'], axis = 1)
df_movies.fillna({'Directors' : "NA"}, inplace = True)
# Cast
df_movies.fillna({'Cast' : "NA"}, inplace = True)
# Genres
df_movies.fillna({'Genres': "NA"}, inplace = True)
# Country
df_movies.fillna({'Country': "NA"}, inplace = True)
# Language
df_movies.fillna({'Language': "NA"}, inplace = True)
# Plotline
df_movies.fillna({'Plotline': "NA"}, inplace = True)
# Runtime
# df_movies.fillna({'Runtime' : df_movies['Runtime'].mean()}, inplace = True)
# df_movies['Runtime'] = df_movies['Runtime'].astype(int)
df_movies.fillna({'Runtime' : "NA"}, inplace = True)
# Kind
# df_movies.fillna({'Kind': "NA"}, inplace = True)
# Type
# df_movies.fillna({'Type': "NA"}, inplace = True)
# df_movies = df_movies.drop(['Type'], axis = 1)
# Seasons
# df_movies.fillna({'Seasons': 1}, inplace = True)
# df_movies.fillna({'Seasons': "NA"}, inplace = True)
df_movies = df_movies.drop(['Seasons'], axis = 1)
# df_movies['Seasons'] = df_movies['Seasons'].astype(int)
# df_movies.fillna({'Seasons' : df_movies['Seasons'].mean()}, inplace = True)
# df_movies['Seasons'] = df_movies['Seasons'].astype(int)
# Service Provider
df_movies['Service Provider'] = df_movies.loc[:, ['Netflix', 'Prime Video', 'Disney+', 'Hulu']].idxmax(axis = 1)
# df_movies.drop(['Netflix','Prime Video','Disney+','Hulu'], axis = 1)
# Removing Duplicate and Missing Entries
df_movies.dropna(how = 'any', inplace = True)
df_movies.drop_duplicates(inplace = True)
data_investigate(df_movies)
No of Rows : 16923
No of Coloums : 20
**************************************************
Colums Names :
Index(['ID', 'Title', 'Year', 'Age', 'IMDb', 'Rotten Tomatoes', 'Directors',
'Cast', 'Genres', 'Country', 'Language', 'Plotline', 'Runtime', 'Kind',
'Netflix', 'Hulu', 'Prime Video', 'Disney+', 'Type',
'Service Provider'],
dtype='object')
**************************************************
Datatype of Columns :
ID int64
Title object
Year int64
Age object
IMDb object
Rotten Tomatoes object
Directors object
Cast object
Genres object
Country object
Language object
Plotline object
Runtime object
Kind object
Netflix int64
Hulu int64
Prime Video int64
Disney+ int64
Type int64
Service Provider object
dtype: object
**************************************************
Missing Values :
Series([], dtype: int64)
**************************************************
Missing vaules %age wise :
ID 0.0
Title 0.0
Year 0.0
Age 0.0
IMDb 0.0
Rotten Tomatoes 0.0
Directors 0.0
Cast 0.0
Genres 0.0
Country 0.0
Language 0.0
Plotline 0.0
Runtime 0.0
Kind 0.0
Netflix 0.0
Hulu 0.0
Prime Video 0.0
Disney+ 0.0
Type 0.0
Service Provider 0.0
dtype: float64
**************************************************
Pictorial Representation :
df_movies.head()
| ID | Title | Year | Age | IMDb | Rotten Tomatoes | Directors | Cast | Genres | Country | Language | Plotline | Runtime | Kind | Netflix | Hulu | Prime Video | Disney+ | Type | Service Provider | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | Inception | 2010 | 13 | 8.8 | 87 | Christopher Nolan | Leonardo DiCaprio,Joseph Gordon-Levitt,Elliot ... | Action,Adventure,Sci-Fi,Thriller | United States,United Kingdom | English,Japanese,French | Dom Cobb is a skilled thief, the absolute best... | 148 | movie | 1 | 0 | 0 | 0 | 0 | Netflix |
| 1 | 2 | The Matrix | 1999 | 16 | 8.7 | 88 | Lana Wachowski,Lilly Wachowski | Keanu Reeves,Laurence Fishburne,Carrie-Anne Mo... | Action,Sci-Fi | United States | English | Thomas A. Anderson is a man living two lives. ... | 136 | movie | 1 | 0 | 0 | 0 | 0 | Netflix |
| 2 | 3 | Avengers: Infinity War | 2018 | 13 | 8.4 | 85 | Anthony Russo,Joe Russo | Robert Downey Jr.,Chris Hemsworth,Mark Ruffalo... | Action,Adventure,Sci-Fi | United States | English | As the Avengers and their allies have continue... | 149 | movie | 1 | 0 | 0 | 0 | 0 | Netflix |
| 3 | 4 | Back to the Future | 1985 | 7 | 8.5 | 96 | Robert Zemeckis | Michael J. Fox,Christopher Lloyd,Lea Thompson,... | Adventure,Comedy,Sci-Fi | United States | English | Marty McFly, a typical American teenager of th... | 116 | movie | 1 | 0 | 0 | 0 | 0 | Netflix |
| 4 | 5 | The Good, the Bad and the Ugly | 1966 | 16 | 8.8 | 97 | Sergio Leone | Eli Wallach,Clint Eastwood,Lee Van Cleef,Aldo ... | Western | Italy,Spain,West Germany,United States | Italian | Blondie (The Good) (Clint Eastwood) is a profe... | 161 | movie | 1 | 0 | 1 | 0 | 0 | Netflix |
df_movies.describe()
| ID | Year | Netflix | Hulu | Prime Video | Disney+ | Type | |
|---|---|---|---|---|---|---|---|
| count | 16923.000000 | 16923.000000 | 16923.000000 | 16923.000000 | 16923.000000 | 16923.000000 | 16923.0 |
| mean | 8462.000000 | 2003.211901 | 0.214915 | 0.062637 | 0.727235 | 0.033150 | 0.0 |
| std | 4885.393638 | 20.526532 | 0.410775 | 0.242315 | 0.445394 | 0.179034 | 0.0 |
| min | 1.000000 | 1901.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.0 |
| 25% | 4231.500000 | 2001.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.0 |
| 50% | 8462.000000 | 2012.000000 | 0.000000 | 0.000000 | 1.000000 | 0.000000 | 0.0 |
| 75% | 12692.500000 | 2016.000000 | 0.000000 | 0.000000 | 1.000000 | 0.000000 | 0.0 |
| max | 16923.000000 | 2020.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 0.0 |
df_movies.corr()
| ID | Year | Netflix | Hulu | Prime Video | Disney+ | Type | |
|---|---|---|---|---|---|---|---|
| ID | 1.000000 | -0.217816 | -0.644470 | -0.129926 | 0.469301 | 0.263530 | NaN |
| Year | -0.217816 | 1.000000 | 0.256151 | 0.101337 | -0.255578 | -0.047258 | NaN |
| Netflix | -0.644470 | 0.256151 | 1.000000 | -0.118032 | -0.745141 | -0.089649 | NaN |
| Hulu | -0.129926 | 0.101337 | -0.118032 | 1.000000 | -0.284654 | -0.039693 | NaN |
| Prime Video | 0.469301 | -0.255578 | -0.745141 | -0.284654 | 1.000000 | -0.289008 | NaN |
| Disney+ | 0.263530 | -0.047258 | -0.089649 | -0.039693 | -0.289008 | 1.000000 | NaN |
| Type | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
# df_movies.sort_values('Year', ascending = True)
# df_movies.sort_values('IMDb', ascending = False)
# df_movies.to_csv(path_or_buf= '/content/drive/MyDrive/Files/updated_ottmovies.csv', index = False)
# path = '/content/drive/MyDrive/Files/'
# udf_movies = pd.read_csv(path + 'updated_ottmovies.csv')
# udf_movies
# df_netflix_movies = df_movies.loc[(df_movies['Netflix'] > 0)]
# df_hulu_movies = df_movies.loc[(df_movies['Hulu'] > 0)]
# df_prime_video_movies = df_movies.loc[(df_movies['Prime Video'] > 0)]
# df_disney_movies = df_movies.loc[(df_movies['Disney+'] > 0)]
df_netflix_only_movies = df_movies[(df_movies['Netflix'] == 1) & (df_movies['Hulu'] == 0) & (df_movies['Prime Video'] == 0 ) & (df_movies['Disney+'] == 0)]
df_hulu_only_movies = df_movies[(df_movies['Netflix'] == 0) & (df_movies['Hulu'] == 1) & (df_movies['Prime Video'] == 0 ) & (df_movies['Disney+'] == 0)]
df_prime_video_only_movies = df_movies[(df_movies['Netflix'] == 0) & (df_movies['Hulu'] == 0) & (df_movies['Prime Video'] == 1 ) & (df_movies['Disney+'] == 0)]
df_disney_only_movies = df_movies[(df_movies['Netflix'] == 0) & (df_movies['Hulu'] == 0) & (df_movies['Prime Video'] == 0 ) & (df_movies['Disney+'] == 1)]
df_movies_languages = df_movies.copy()
df_movies_languages.drop(df_movies_languages.loc[df_movies_languages['Language'] == "NA"].index, inplace = True)
# df_movies_languages = df_movies_languages[df_movies_languages.Language != "NA"]
# df_movies_languages['Language'] = df_movies_languages['Language'].astype(str)
df_movies_count_languages = df_movies_languages.copy()
df_movies_language = df_movies_languages.copy()
# Create languages dict where key=name and value = number of languages
languages = {}
for i in df_movies_count_languages['Language'].dropna():
if i != "NA":
#print(i,len(i.split(',')))
languages[i] = len(i.split(','))
else:
languages[i] = 0
# Add this information to our dataframe as a new column
df_movies_count_languages['Number of Languages'] = df_movies_count_languages['Language'].map(languages).astype(int)
df_movies_mixed_languages = df_movies_count_languages.copy()
# Creating distinct dataframes only with the movies present on individual streaming platforms
netflix_languages_movies = df_movies_count_languages.loc[df_movies_count_languages['Netflix'] == 1]
hulu_languages_movies = df_movies_count_languages.loc[df_movies_count_languages['Hulu'] == 1]
prime_video_languages_movies = df_movies_count_languages.loc[df_movies_count_languages['Prime Video'] == 1]
disney_languages_movies = df_movies_count_languages.loc[df_movies_count_languages['Disney+'] == 1]
plt.figure(figsize = (10, 10))
corr = df_movies_count_languages.corr()
# Plot figsize
fig, ax = plt.subplots(figsize=(10, 8))
# Generate Heat Map, alleast annotations and place floats in map
sns.heatmap(corr, cmap = 'magma', annot = True, fmt = ".2f")
# Apply xticks
plt.xticks(range(len(corr.columns)), corr.columns);
# Apply yticks
plt.yticks(range(len(corr.columns)), corr.columns)
# show plot
plt.show()
fig.show()
<Figure size 720x720 with 0 Axes>
df_languages_most_movies = df_movies_count_languages.sort_values(by = 'Number of Languages', ascending = False).reset_index()
df_languages_most_movies = df_languages_most_movies.drop(['index'], axis = 1)
# filter = (df_movies_count_languages['Number of Languages'] == (df_movies_count_languages['Number of Languages'].max()))
# df_languages_most_movies = df_movies_count_languages[filter]
# mostest_rated_movies = df_movies_count_languages.loc[df_movies_count_languages['Number of Languages'].idxmax()]
print('\nMovies with Highest Ever Number of Languages are : \n')
df_languages_most_movies.head(5)
Movies with Highest Ever Number of Languages are :
| ID | Title | Year | Age | IMDb | Rotten Tomatoes | Directors | Cast | Genres | Country | ... | Plotline | Runtime | Kind | Netflix | Hulu | Prime Video | Disney+ | Type | Service Provider | Number of Languages | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 15782 | The Jungle Book | 1967 | 7 | 7.4 | 94 | Jon Favreau | Neel Sethi,Bill Murray,Ben Kingsley,Idris Elba... | Adventure,Drama,Family,Fantasy | United Kingdom,United States | ... | While the First Order continues to ravage the ... | 106 | movie | 0 | 0 | 0 | 1 | 0 | Disney+ | 16 |
| 1 | 456 | 2012 | 2009 | 13 | 5.8 | 39 | Roland Emmerich | John Cusack,Amanda Peet,Chiwetel Ejiofor,Thand... | Action,Adventure,Sci-Fi | United States | ... | Dr. Adrian Helmsley, part of a worldwide geoph... | 158 | movie | 1 | 0 | 0 | 0 | 0 | Netflix | 10 |
| 2 | 3940 | Free Willy 2: The Adventure Home | 1995 | 7 | 5.1 | 50 | Dwight H. Little | Jason James Richter,Francis Capra,Mary Kate Sc... | Adventure,Drama,Family | France,United States,Luxembourg | ... | Willy the smart and rebellious whale and Jessi... | 95 | movie | 0 | 1 | 0 | 0 | 0 | Hulu | 8 |
| 3 | 555 | American Assassin | 2017 | 16 | 6.2 | 34 | Michael Cuesta | Dylan O'Brien,Charlotte Vega,Christopher Bomfo... | Action,Thriller | United States | ... | Twenty three-year-old Mitch lost his parents t... | 111 | movie | 1 | 0 | 0 | 0 | 0 | Netflix | 8 |
| 4 | 449 | Die Another Day | 2002 | 13 | 6.1 | 56 | Lee Tamahori | Pierce Brosnan,Halle Berry,Toby Stephens,Rosam... | Action,Adventure,Thriller | United Kingdom,United States,Spain,Iceland | ... | Pierce Brosnan gives one last mission as James... | 133 | movie | 1 | 0 | 0 | 0 | 0 | Netflix | 8 |
5 rows × 21 columns
fig = px.bar(y = df_languages_most_movies['Title'][:15],
x = df_languages_most_movies['Number of Languages'][:15],
color = df_languages_most_movies['Number of Languages'][:15],
color_continuous_scale = 'Teal_r',
labels = { 'y' : 'Movies', 'x' : 'Number of Languages'},
title = 'Movies with Highest Number of Languages : All Platforms')
fig.update_layout(plot_bgcolor = 'white')
fig.show()
df_languages_least_movies = df_movies_count_languages.sort_values(by = 'Number of Languages', ascending = True).reset_index()
df_languages_least_movies = df_languages_least_movies.drop(['index'], axis = 1)
# filter = (df_movies_count_languages['Number of Languages'] == (df_movies_count_languages['Number of Languages'].min()))
# df_languages_least_movies = df_movies_count_languages[filter]
print('\nMovies with Lowest Ever Number of Languages are : \n')
df_languages_least_movies.head(5)
Movies with Lowest Ever Number of Languages are :
| ID | Title | Year | Age | IMDb | Rotten Tomatoes | Directors | Cast | Genres | Country | ... | Plotline | Runtime | Kind | Netflix | Hulu | Prime Video | Disney+ | Type | Service Provider | Number of Languages | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 8405 | Parasomnia | 2008 | 16 | 5.2 | NA | William Malone | Sean Young,Patrick Kilpatrick,Dylan McKnight,C... | Horror,Thriller | United States | ... | Autopsy examines how forensic examiners can he... | 103 | movie | 0 | 0 | 1 | 0 | 0 | Prime Video | 1 |
| 1 | 10849 | Road to the Open | 2014 | 7 | 6.7 | NA | Cole Claassen | Troy McKay,Phillip DeVona,Michelle Gunn,Judd N... | Comedy,Drama | United States | ... | Having a child die is the worst thing imaginab... | 90 | movie | 0 | 0 | 1 | 0 | 0 | Prime Video | 1 |
| 2 | 10850 | Blinder | 2013 | NR | 4.5 | 0 | Richard Gray | Oliver Ackland,Jack Thompson,Zoe Carides,Anna ... | Drama,Romance,Sport | Australia | ... | Bernard and Claire Boiko won enough money on T... | 102 | movie | 0 | 0 | 1 | 0 | 0 | Prime Video | 1 |
| 3 | 10851 | The Last Hit Man | 2008 | NR | 5.3 | NA | Christopher Warre Smets | Joe Mantegna,Elizabeth Whitmere,Romano Orzari,... | Action,Crime,Drama,Thriller | Canada | ... | AND THEN THERE WAS LIGHT tells the story of Ba... | 90 | movie | 0 | 0 | 1 | 0 | 0 | Prime Video | 1 |
| 4 | 10852 | The Kung Fu Instructor | 1979 | 16 | 6.4 | NA | Chung Sun | Lung Ti,Yue Wong,Feng Ku,Angie Chiu,Lung-Wei W... | Action,Drama | Hong Kong | ... | NA | 100 | movie | 0 | 0 | 1 | 0 | 0 | Prime Video | 1 |
5 rows × 21 columns
fig = px.bar(y = df_languages_least_movies['Title'][:15],
x = df_languages_least_movies['Number of Languages'][:15],
color = df_languages_least_movies['Number of Languages'][:15],
color_continuous_scale = 'Teal_r',
labels = { 'y' : 'Movies', 'x' : 'Number of Languages'},
title = 'Movies with Lowest Number of Languages : All Platforms')
fig.update_layout(plot_bgcolor = 'white')
fig.show()
print(f'''
Total '{df_movies_count_languages['Number of Languages'].unique().shape[0]}' unique Number of Languages s were Given, They were Like this,\n
{df_movies_count_languages.sort_values(by = 'Number of Languages', ascending = False)['Number of Languages'].unique()}\n
The Highest Number of Languages Ever Any Movie Got is '{df_languages_most_movies['Title'][0]}' : '{df_languages_most_movies['Number of Languages'].max()}'\n
The Lowest Number of Languages Ever Any Movie Got is '{df_languages_least_movies['Title'][0]}' : '{df_languages_least_movies['Number of Languages'].min()}'\n
''')
Total '10' unique Number of Languages s were Given, They were Like this,
[16 10 8 7 6 5 4 3 2 1]
The Highest Number of Languages Ever Any Movie Got is 'The Jungle Book' : '16'
The Lowest Number of Languages Ever Any Movie Got is 'Parasomnia' : '1'
netflix_languages_most_movies = df_languages_most_movies.loc[df_languages_most_movies['Netflix']==1].reset_index()
netflix_languages_most_movies = netflix_languages_most_movies.drop(['index'], axis = 1)
netflix_languages_least_movies = df_languages_least_movies.loc[df_languages_least_movies['Netflix']==1].reset_index()
netflix_languages_least_movies = netflix_languages_least_movies.drop(['index'], axis = 1)
netflix_languages_most_movies.head(5)
| ID | Title | Year | Age | IMDb | Rotten Tomatoes | Directors | Cast | Genres | Country | ... | Plotline | Runtime | Kind | Netflix | Hulu | Prime Video | Disney+ | Type | Service Provider | Number of Languages | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 456 | 2012 | 2009 | 13 | 5.8 | 39 | Roland Emmerich | John Cusack,Amanda Peet,Chiwetel Ejiofor,Thand... | Action,Adventure,Sci-Fi | United States | ... | Dr. Adrian Helmsley, part of a worldwide geoph... | 158 | movie | 1 | 0 | 0 | 0 | 0 | Netflix | 10 |
| 1 | 555 | American Assassin | 2017 | 16 | 6.2 | 34 | Michael Cuesta | Dylan O'Brien,Charlotte Vega,Christopher Bomfo... | Action,Thriller | United States | ... | Twenty three-year-old Mitch lost his parents t... | 111 | movie | 1 | 0 | 0 | 0 | 0 | Netflix | 8 |
| 2 | 449 | Die Another Day | 2002 | 13 | 6.1 | 56 | Lee Tamahori | Pierce Brosnan,Halle Berry,Toby Stephens,Rosam... | Action,Adventure,Thriller | United Kingdom,United States,Spain,Iceland | ... | Pierce Brosnan gives one last mission as James... | 133 | movie | 1 | 0 | 0 | 0 | 0 | Netflix | 8 |
| 3 | 511 | The Wandering Earth | 2019 | 13 | 6 | 70 | Frant Gwo | Jing Wu,Chuxiao Qu,Guangjie Li,Man-Tat Ng,Jin ... | Action,Sci-Fi | China | ... | The sun is dying out. The earth will soon be e... | 125 | movie | 1 | 0 | 0 | 0 | 0 | Netflix | 8 |
| 4 | 144 | Babel | 2006 | 16 | 7.4 | 69 | Alejandro G. Iñárritu | Brad Pitt,Cate Blanchett,Mohamed Akhzam,Peter ... | Drama | United States,Mexico,France,Morocco,Japan | ... | 4 interlocking stories connected by a single g... | 143 | movie | 1 | 0 | 0 | 0 | 0 | Netflix | 8 |
5 rows × 21 columns
fig = px.bar(y = netflix_languages_most_movies['Title'][:15],
x = netflix_languages_most_movies['Number of Languages'][:15],
color = netflix_languages_most_movies['Number of Languages'][:15],
color_continuous_scale = 'Teal_r',
labels = { 'y' : 'Movies', 'x' : 'Number of Languages'},
title = 'Movies with Highest Number of Languages : Netflix')
fig.update_layout(plot_bgcolor = 'white')
fig.show()
fig = px.bar(y = netflix_languages_least_movies['Title'][:15],
x = netflix_languages_least_movies['Number of Languages'][:15],
color = netflix_languages_least_movies['Number of Languages'][:15],
color_continuous_scale = 'Teal_r',
labels = { 'y' : 'Movies', 'x' : 'Number of Languages'},
title = 'Movies with Lowest Number of Languages : Netflix')
fig.update_layout(plot_bgcolor = 'white')
fig.show()
hulu_languages_most_movies = df_languages_most_movies.loc[df_languages_most_movies['Hulu']==1].reset_index()
hulu_languages_most_movies = hulu_languages_most_movies.drop(['index'], axis = 1)
hulu_languages_least_movies = df_languages_least_movies.loc[df_languages_least_movies['Hulu']==1].reset_index()
hulu_languages_least_movies = hulu_languages_least_movies.drop(['index'], axis = 1)
hulu_languages_most_movies.head(5)
| ID | Title | Year | Age | IMDb | Rotten Tomatoes | Directors | Cast | Genres | Country | ... | Plotline | Runtime | Kind | Netflix | Hulu | Prime Video | Disney+ | Type | Service Provider | Number of Languages | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 3940 | Free Willy 2: The Adventure Home | 1995 | 7 | 5.1 | 50 | Dwight H. Little | Jason James Richter,Francis Capra,Mary Kate Sc... | Adventure,Drama,Family | France,United States,Luxembourg | ... | Willy the smart and rebellious whale and Jessi... | 95 | movie | 0 | 1 | 0 | 0 | 0 | Hulu | 8 |
| 1 | 3777 | Downsizing | 2017 | 16 | 5.7 | 47 | Alexander Payne | Matt Damon,Christoph Waltz,Hong Chau,Kristen W... | Drama,Fantasy,Sci-Fi | United States,Norway | ... | A new world of possibilities awaits, thanks to... | 135 | movie | 0 | 1 | 1 | 0 | 0 | Prime Video | 7 |
| 2 | 3523 | Embrace of the Serpent | 2015 | NR | 7.9 | 96 | Ciro Guerra | Nilbio Torres,Antonio Bolívar,Jan Bijvoet,Brio... | Adventure,Biography,Drama | Colombia,Venezuela,Argentina | ... | NA | 125 | movie | 0 | 1 | 1 | 0 | 0 | Prime Video | 7 |
| 3 | 16424 | The Exorcist | 2016 | 16 | 8 | 89 | William Friedkin | Ellen Burstyn,Max von Sydow,Lee J. Cobb,Kitty ... | Horror | United States | ... | NA | 122 | movie | 0 | 1 | 0 | 0 | 0 | Hulu | 7 |
| 4 | 3916 | Terminal | 2018 | 13 | 7.4 | 33 | Steven Spielberg | Tom Hanks,Catherine Zeta-Jones,Stanley Tucci,C... | Comedy,Drama,Romance | United States | ... | Victor Navorski reaches JFK airport from a pol... | 128 | movie | 0 | 1 | 0 | 0 | 0 | Hulu | 7 |
5 rows × 21 columns
fig = px.bar(y = hulu_languages_most_movies['Title'][:15],
x = hulu_languages_most_movies['Number of Languages'][:15],
color = hulu_languages_most_movies['Number of Languages'][:15],
color_continuous_scale = 'Teal_r',
labels = { 'y' : 'Movies', 'x' : 'Number of Languages'},
title = 'Movies with Highest Number of Languages : Hulu')
fig.update_layout(plot_bgcolor = 'white')
fig.show()
fig = px.bar(y = hulu_languages_least_movies['Title'][:15],
x = hulu_languages_least_movies['Number of Languages'][:15],
color = hulu_languages_least_movies['Number of Languages'][:15],
color_continuous_scale = 'Teal_r',
labels = { 'y' : 'Movies', 'x' : 'Number of Languages'},
title = 'Movies with Lowest Number of Languages : Hulu')
fig.update_layout(plot_bgcolor = 'white')
fig.show()
prime_video_languages_most_movies = df_languages_most_movies.loc[df_languages_most_movies['Prime Video']==1].reset_index()
prime_video_languages_most_movies = prime_video_languages_most_movies.drop(['index'], axis = 1)
prime_video_languages_least_movies = df_languages_least_movies.loc[df_languages_least_movies['Prime Video']==1].reset_index()
prime_video_languages_least_movies = prime_video_languages_least_movies.drop(['index'], axis = 1)
prime_video_languages_most_movies.head(5)
| ID | Title | Year | Age | IMDb | Rotten Tomatoes | Directors | Cast | Genres | Country | ... | Plotline | Runtime | Kind | Netflix | Hulu | Prime Video | Disney+ | Type | Service Provider | Number of Languages | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 3777 | Downsizing | 2017 | 16 | 5.7 | 47 | Alexander Payne | Matt Damon,Christoph Waltz,Hong Chau,Kristen W... | Drama,Fantasy,Sci-Fi | United States,Norway | ... | A new world of possibilities awaits, thanks to... | 135 | movie | 0 | 1 | 1 | 0 | 0 | Prime Video | 7 |
| 1 | 8500 | Crash Test Aglaé | 2017 | NR | 6.6 | NA | Eric Gravel | India Hair,Julie Depardieu,Yolande Moreau,Anne... | Comedy,Drama | France | ... | Spetnaz (Special Ops) veteran Nick Cherenko le... | 85 | movie | 0 | 0 | 1 | 0 | 0 | Prime Video | 7 |
| 2 | 11459 | Off Jackson Avenue | 2008 | NR | 6.7 | 59 | John-Luke Montias | Jessica Pimentel,Stivi Paskoski,Jun Suenaga,Jo... | Crime,Drama,Thriller | United States | ... | The Attendant tells the story of Alex, a haple... | 80 | movie | 0 | 0 | 1 | 0 | 0 | Prime Video | 7 |
| 3 | 4866 | The Vikings | 1958 | 16 | 6.6 | 76 | John McTiernan,Michael Crichton | Antonio Banderas,Diane Venora,Dennis Storhøi,V... | Action,Adventure,History | United States | ... | NA | 102 | movie | 0 | 0 | 1 | 0 | 0 | Prime Video | 7 |
| 4 | 3523 | Embrace of the Serpent | 2015 | NR | 7.9 | 96 | Ciro Guerra | Nilbio Torres,Antonio Bolívar,Jan Bijvoet,Brio... | Adventure,Biography,Drama | Colombia,Venezuela,Argentina | ... | NA | 125 | movie | 0 | 1 | 1 | 0 | 0 | Prime Video | 7 |
5 rows × 21 columns
fig = px.bar(y = prime_video_languages_most_movies['Title'][:15],
x = prime_video_languages_most_movies['Number of Languages'][:15],
color = prime_video_languages_most_movies['Number of Languages'][:15],
color_continuous_scale = 'Teal_r',
labels = { 'y' : 'Movies', 'x' : 'Number of Languages'},
title = 'Movies with Highest Number of Languages : Prime Video')
fig.update_layout(plot_bgcolor = 'white')
fig.show()
fig = px.bar(y = prime_video_languages_least_movies['Title'][:15],
x = prime_video_languages_least_movies['Number of Languages'][:15],
color = prime_video_languages_least_movies['Number of Languages'][:15],
color_continuous_scale = 'Teal_r',
labels = { 'y' : 'Movies', 'x' : 'Number of Languages'},
title = 'Movies with Lowest Number of Languages : Prime Video')
fig.update_layout(plot_bgcolor = 'white')
fig.show()
disney_languages_most_movies = df_languages_most_movies.loc[df_languages_most_movies['Disney+']==1].reset_index()
disney_languages_most_movies = disney_languages_most_movies.drop(['index'], axis = 1)
disney_languages_least_movies = df_languages_least_movies.loc[df_languages_least_movies['Disney+']==1].reset_index()
disney_languages_least_movies = disney_languages_least_movies.drop(['index'], axis = 1)
disney_languages_most_movies.head(5)
| ID | Title | Year | Age | IMDb | Rotten Tomatoes | Directors | Cast | Genres | Country | ... | Plotline | Runtime | Kind | Netflix | Hulu | Prime Video | Disney+ | Type | Service Provider | Number of Languages | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 15782 | The Jungle Book | 1967 | 7 | 7.4 | 94 | Jon Favreau | Neel Sethi,Bill Murray,Ben Kingsley,Idris Elba... | Adventure,Drama,Family,Fantasy | United Kingdom,United States | ... | While the First Order continues to ravage the ... | 106 | movie | 0 | 0 | 0 | 1 | 0 | Disney+ | 16 |
| 1 | 16916 | Iron Man | 1994 | 16 | 7.9 | 60 | Jon Favreau | Robert Downey Jr.,Terrence Howard,Jeff Bridges... | Action,Adventure,Sci-Fi | United States,Canada | ... | NA | 126 | movie | 0 | 0 | 0 | 1 | 0 | Disney+ | 7 |
| 2 | 15746 | Captain America: Civil War | 2016 | 13 | 7.8 | 90 | Anthony Russo,Joe Russo | Chris Evans,Robert Downey Jr.,Scarlett Johanss... | Action,Adventure,Sci-Fi | United States | ... | All looks lost for the Rebellion against the E... | 147 | movie | 0 | 0 | 0 | 1 | 0 | Disney+ | 6 |
| 3 | 15802 | Miracle on 34th Street | 1947 | 7 | 6.6 | 96 | Les Mayfield | Richard Attenborough,Elizabeth Perkins,Dylan M... | Family,Fantasy | United States | ... | A young boy, Pete, is found in a forest where ... | 114 | movie | 0 | 0 | 0 | 1 | 0 | Disney+ | 6 |
| 4 | 16073 | The Brave Little Toaster to the Rescue | 1997 | 0 | 6.4 | 40 | Robert C. Ramirez,Patrick A. Ventura | Jessica Tuck,Chris Young,Roger Kabler,Deanna O... | Animation,Adventure,Family,Fantasy | United States | ... | Freshman Jamie Bartlett is frustrated with hig... | 74 | movie | 0 | 0 | 0 | 1 | 0 | Disney+ | 6 |
5 rows × 21 columns
fig = px.bar(y = disney_languages_most_movies['Title'][:15],
x = disney_languages_most_movies['Number of Languages'][:15],
color = disney_languages_most_movies['Number of Languages'][:15],
color_continuous_scale = 'Teal_r',
labels = { 'y' : 'Movies', 'x' : 'Number of Languages'},
title = 'Movies with Highest Number of Languages : Disney+')
fig.update_layout(plot_bgcolor = 'white')
fig.show()
fig = px.bar(y = disney_languages_least_movies['Title'][:15],
x = disney_languages_least_movies['Number of Languages'][:15],
color = disney_languages_least_movies['Number of Languages'][:15],
color_continuous_scale = 'Teal_r',
labels = { 'y' : 'Movies', 'x' : 'Number of Languages'},
title = 'Movies with Lowest Number of Languages : Disney+')
fig.update_layout(plot_bgcolor = 'white')
fig.show()
print(f'''
The Movie with Highest Number of Languages Ever Got is '{df_languages_most_movies['Title'][0]}' : '{df_languages_most_movies['Number of Languages'].max()}'\n
The Movie with Lowest Number of Languages Ever Got is '{df_languages_least_movies['Title'][0]}' : '{df_languages_least_movies['Number of Languages'].min()}'\n
The Movie with Highest Number of Languages on 'Netflix' is '{netflix_languages_most_movies['Title'][0]}' : '{netflix_languages_most_movies['Number of Languages'].max()}'\n
The Movie with Lowest Number of Languages on 'Netflix' is '{netflix_languages_least_movies['Title'][0]}' : '{netflix_languages_least_movies['Number of Languages'].min()}'\n
The Movie with Highest Number of Languages on 'Hulu' is '{hulu_languages_most_movies['Title'][0]}' : '{hulu_languages_most_movies['Number of Languages'].max()}'\n
The Movie with Lowest Number of Languages on 'Hulu' is '{hulu_languages_least_movies['Title'][0]}' : '{hulu_languages_least_movies['Number of Languages'].min()}'\n
The Movie with Highest Number of Languages on 'Prime Video' is '{prime_video_languages_most_movies['Title'][0]}' : '{prime_video_languages_most_movies['Number of Languages'].max()}'\n
The Movie with Lowest Number of Languages on 'Prime Video' is '{prime_video_languages_least_movies['Title'][0]}' : '{prime_video_languages_least_movies['Number of Languages'].min()}'\n
The Movie with Highest Number of Languages on 'Disney+' is '{disney_languages_most_movies['Title'][0]}' : '{disney_languages_most_movies['Number of Languages'].max()}'\n
The Movie with Lowest Number of Languages on 'Disney+' is '{disney_languages_least_movies['Title'][0]}' : '{disney_languages_least_movies['Number of Languages'].min()}'\n
''')
The Movie with Highest Number of Languages Ever Got is 'The Jungle Book' : '16'
The Movie with Lowest Number of Languages Ever Got is 'Parasomnia' : '1'
The Movie with Highest Number of Languages on 'Netflix' is '2012' : '10'
The Movie with Lowest Number of Languages on 'Netflix' is 'Iris' : '1'
The Movie with Highest Number of Languages on 'Hulu' is 'Free Willy 2: The Adventure Home' : '8'
The Movie with Lowest Number of Languages on 'Hulu' is 'The Impostor' : '1'
The Movie with Highest Number of Languages on 'Prime Video' is 'Downsizing' : '7'
The Movie with Lowest Number of Languages on 'Prime Video' is 'Parasomnia' : '1'
The Movie with Highest Number of Languages on 'Disney+' is 'The Jungle Book' : '16'
The Movie with Lowest Number of Languages on 'Disney+' is 'The Swap' : '1'
print(f'''
Accross All Platforms the Average Number of Languages is '{round(df_movies_count_languages['Number of Languages'].mean(), ndigits = 2)}'\n
The Average Number of Languages on 'Netflix' is '{round(netflix_languages_movies['Number of Languages'].mean(), ndigits = 2)}'\n
The Average Number of Languages on 'Hulu' is '{round(hulu_languages_movies['Number of Languages'].mean(), ndigits = 2)}'\n
The Average Number of Languages on 'Prime Video' is '{round(prime_video_languages_movies['Number of Languages'].mean(), ndigits = 2)}'\n
The Average Number of Languages on 'Disney+' is '{round(disney_languages_movies['Number of Languages'].mean(), ndigits = 2)}'\n
''')
Accross All Platforms the Average Number of Languages is '1.28'
The Average Number of Languages on 'Netflix' is '1.39'
The Average Number of Languages on 'Hulu' is '1.47'
The Average Number of Languages on 'Prime Video' is '1.24'
The Average Number of Languages on 'Disney+' is '1.33'
print(f'''
Accross All Platforms Total Count of Language is '{df_movies_count_languages['Number of Languages'].max()}'\n
Total Count of Language on 'Netflix' is '{netflix_languages_movies['Number of Languages'].max()}'\n
Total Count of Language on 'Hulu' is '{hulu_languages_movies['Number of Languages'].max()}'\n
Total Count of Language on 'Prime Video' is '{prime_video_languages_movies['Number of Languages'].max()}'\n
Total Count of Language on 'Disney+' is '{disney_languages_movies['Number of Languages'].max()}'\n
''')
Accross All Platforms Total Count of Language is '16'
Total Count of Language on 'Netflix' is '10'
Total Count of Language on 'Hulu' is '8'
Total Count of Language on 'Prime Video' is '7'
Total Count of Language on 'Disney+' is '16'
f, ax = plt.subplots(1, 2 , figsize = (20, 5))
sns.distplot(df_movies_count_languages['Number of Languages'],bins = 20, kde = True, ax = ax[0])
sns.boxplot(df_movies_count_languages['Number of Languages'], ax = ax[1])
plt.show()
# Defining plot size and title
plt.figure(figsize = (20, 5))
plt.title('Number of Languages s Per Platform')
# Plotting the information from each dataset into a histogram
sns.histplot(prime_video_languages_movies['Number of Languages'], color = 'lightblue', legend = True, kde = True)
sns.histplot(netflix_languages_movies['Number of Languages'], color = 'red', legend = True, kde = True)
sns.histplot(hulu_languages_movies['Number of Languages'], color = 'lightgreen', legend = True, kde = True)
sns.histplot(disney_languages_movies['Number of Languages'], color = 'darkblue', legend = True, kde = True)
# Setting the legend
plt.legend(['Prime Video', 'Netflix', 'Hulu', 'Disney+'])
plt.show()
df_lan = df_movies_language['Language'].str.split(',').apply(pd.Series).stack()
del df_movies_language['Language']
df_lan.index = df_lan.index.droplevel(-1)
df_lan.name = 'Language'
df_movies_language = df_movies_language.join(df_lan)
df_movies_language.drop_duplicates(inplace = True)
df_movies_language.head(5)
| ID | Title | Year | Age | IMDb | Rotten Tomatoes | Directors | Cast | Genres | Country | Plotline | Runtime | Kind | Netflix | Hulu | Prime Video | Disney+ | Type | Service Provider | Language | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | Inception | 2010 | 13 | 8.8 | 87 | Christopher Nolan | Leonardo DiCaprio,Joseph Gordon-Levitt,Elliot ... | Action,Adventure,Sci-Fi,Thriller | United States,United Kingdom | Dom Cobb is a skilled thief, the absolute best... | 148 | movie | 1 | 0 | 0 | 0 | 0 | Netflix | English |
| 0 | 1 | Inception | 2010 | 13 | 8.8 | 87 | Christopher Nolan | Leonardo DiCaprio,Joseph Gordon-Levitt,Elliot ... | Action,Adventure,Sci-Fi,Thriller | United States,United Kingdom | Dom Cobb is a skilled thief, the absolute best... | 148 | movie | 1 | 0 | 0 | 0 | 0 | Netflix | Japanese |
| 0 | 1 | Inception | 2010 | 13 | 8.8 | 87 | Christopher Nolan | Leonardo DiCaprio,Joseph Gordon-Levitt,Elliot ... | Action,Adventure,Sci-Fi,Thriller | United States,United Kingdom | Dom Cobb is a skilled thief, the absolute best... | 148 | movie | 1 | 0 | 0 | 0 | 0 | Netflix | French |
| 1 | 2 | The Matrix | 1999 | 16 | 8.7 | 88 | Lana Wachowski,Lilly Wachowski | Keanu Reeves,Laurence Fishburne,Carrie-Anne Mo... | Action,Sci-Fi | United States | Thomas A. Anderson is a man living two lives. ... | 136 | movie | 1 | 0 | 0 | 0 | 0 | Netflix | English |
| 2 | 3 | Avengers: Infinity War | 2018 | 13 | 8.4 | 85 | Anthony Russo,Joe Russo | Robert Downey Jr.,Chris Hemsworth,Mark Ruffalo... | Action,Adventure,Sci-Fi | United States | As the Avengers and their allies have continue... | 149 | movie | 1 | 0 | 0 | 0 | 0 | Netflix | English |
language_count = df_movies_language.groupby('Language')['Title'].count()
language_movies = df_movies_language.groupby('Language')[['Netflix', 'Hulu', 'Prime Video', 'Disney+']].sum()
language_data_movies = pd.concat([language_count, language_movies], axis = 1).reset_index().rename(columns = {'Title' : 'Movies Count'})
language_data_movies = language_data_movies.sort_values(by = 'Movies Count', ascending = False)
# Language with Movies Counts - All Platforms Combined
language_data_movies.sort_values(by = 'Movies Count', ascending = False)[:10]
| Language | Movies Count | Netflix | Hulu | Prime Video | Disney+ | |
|---|---|---|---|---|---|---|
| 46 | English | 13524 | 2370 | 950 | 10146 | 556 |
| 156 | Spanish | 953 | 356 | 88 | 528 | 23 |
| 53 | French | 875 | 239 | 102 | 553 | 28 |
| 69 | Hindi | 720 | 368 | 6 | 410 | 7 |
| 58 | German | 526 | 118 | 53 | 361 | 16 |
| 77 | Italian | 491 | 87 | 32 | 378 | 11 |
| 102 | Mandarin | 371 | 91 | 21 | 276 | 9 |
| 78 | Japanese | 340 | 101 | 49 | 199 | 5 |
| 139 | Russian | 238 | 60 | 24 | 159 | 6 |
| 11 | Arabic | 196 | 91 | 12 | 93 | 5 |
fig = px.bar(x = language_data_movies['Language'][:50],
y = language_data_movies['Movies Count'][:50],
color = language_data_movies['Movies Count'][:50],
color_continuous_scale = 'Teal_r',
labels = { 'x' : 'Language', 'y' : 'Movies Count'},
title = 'Major Languages : All Platforms')
fig.update_layout(plot_bgcolor = 'white')
fig.show()
df_language_high_movies = language_data_movies.sort_values(by = 'Movies Count', ascending = False).reset_index()
df_language_high_movies = df_language_high_movies.drop(['index'], axis = 1)
# filter = (language_data_movies['Movies Count'] == (language_data_movies['Movies Count'].max()))
# df_language_high_movies = language_data_movies[filter]
# highest_rated_movies = language_data_movies.loc[language_data_movies['Movies Count'].idxmax()]
print('\nLanguage with Highest Ever Movies Count are : All Platforms Combined\n')
df_language_high_movies.head(5)
Language with Highest Ever Movies Count are : All Platforms Combined
| Language | Movies Count | Netflix | Hulu | Prime Video | Disney+ | |
|---|---|---|---|---|---|---|
| 0 | English | 13524 | 2370 | 950 | 10146 | 556 |
| 1 | Spanish | 953 | 356 | 88 | 528 | 23 |
| 2 | French | 875 | 239 | 102 | 553 | 28 |
| 3 | Hindi | 720 | 368 | 6 | 410 | 7 |
| 4 | German | 526 | 118 | 53 | 361 | 16 |
fig = px.bar(y = df_language_high_movies['Language'][:15],
x = df_language_high_movies['Movies Count'][:15],
color = df_language_high_movies['Movies Count'][:15],
color_continuous_scale = 'Teal_r',
labels = { 'y' : 'Language', 'x' : 'Movies Count'},
title = 'Language with Highest Movies : All Platforms')
fig.update_layout(plot_bgcolor = 'white')
fig.show()
df_language_low_movies = language_data_movies.sort_values(by = 'Movies Count', ascending = True).reset_index()
df_language_low_movies = df_language_low_movies.drop(['index'], axis = 1)
# filter = (language_data_movies['Movies Count'] == (language_data_movies['Movies Count'].min()))
# df_language_low_movies = language_data_movies[filter]
print('\nLanguage with Lowest Ever Movies Count are : All Platforms Combined\n')
df_language_low_movies.head(5)
Language with Lowest Ever Movies Count are : All Platforms Combined
| Language | Movies Count | Netflix | Hulu | Prime Video | Disney+ | |
|---|---|---|---|---|---|---|
| 0 | Lao | 1 | 0 | 0 | 1 | 0 |
| 1 | Belarusian | 1 | 0 | 0 | 1 | 0 |
| 2 | Bemba | 1 | 0 | 1 | 0 | 0 |
| 3 | Hakka | 1 | 0 | 0 | 1 | 0 |
| 4 | Berber languages | 1 | 1 | 0 | 0 | 0 |
fig = px.bar(y = df_language_low_movies['Language'][:15],
x = df_language_low_movies['Movies Count'][:15],
color = df_language_low_movies['Movies Count'][:15],
color_continuous_scale = 'Teal_r',
labels = { 'y' : 'Language', 'x' : 'Movies Count'},
title = 'Language with Lowest Movies Count : All Platforms')
fig.update_layout(plot_bgcolor = 'white')
fig.show()
print(f'''
Total '{language_data_movies['Language'].unique().shape[0]}' unique Language Count s were Given, They were Like this,\n
{language_data_movies.sort_values(by = 'Movies Count', ascending = False)['Language'].unique()[:5]}\n
The Highest Ever Movies Count Ever Any Movie Got is '{df_language_high_movies['Language'][0]}' : '{df_language_high_movies['Movies Count'].max()}'\n
The Lowest Ever Movies Count Ever Any Movie Got is '{df_language_low_movies['Language'][0]}' : '{df_language_low_movies['Movies Count'].min()}'\n
''')
Total '183' unique Language Count s were Given, They were Like this,
['English' 'Spanish' 'French' 'Hindi' 'German']
The Highest Ever Movies Count Ever Any Movie Got is 'English' : '13524'
The Lowest Ever Movies Count Ever Any Movie Got is 'Lao' : '1'
fig = px.pie(language_data_movies[:10], names = 'Language', values = 'Movies Count', color_discrete_sequence = px.colors.sequential.Teal)
fig.update_traces(textposition = 'inside', textinfo = 'percent+label', title = 'Movies Count based on Language')
fig.show()
# netflix_language_movies = language_data_movies[language_data_movies['Netflix'] != 0].sort_values(by = 'Netflix', ascending = False).reset_index()
# netflix_language_movies = netflix_language_movies.drop(['index', 'Hulu', 'Prime Video', 'Disney+', 'Movies Count'], axis = 1)
netflix_language_high_movies = df_language_high_movies.sort_values(by = 'Netflix', ascending = False).reset_index()
netflix_language_high_movies = netflix_language_high_movies.drop(['index'], axis = 1)
netflix_language_low_movies = df_language_high_movies.sort_values(by = 'Netflix', ascending = True).reset_index()
netflix_language_low_movies = netflix_language_low_movies.drop(['index'], axis = 1)
netflix_language_high_movies.head(5)
| Language | Movies Count | Netflix | Hulu | Prime Video | Disney+ | |
|---|---|---|---|---|---|---|
| 0 | English | 13524 | 2370 | 950 | 10146 | 556 |
| 1 | Hindi | 720 | 368 | 6 | 410 | 7 |
| 2 | Spanish | 953 | 356 | 88 | 528 | 23 |
| 3 | French | 875 | 239 | 102 | 553 | 28 |
| 4 | German | 526 | 118 | 53 | 361 | 16 |
fig = px.bar(x = netflix_language_high_movies['Language'][:15],
y = netflix_language_high_movies['Netflix'][:15],
color = netflix_language_high_movies['Netflix'][:15],
color_continuous_scale = 'Teal_r',
labels = { 'y' : 'Language', 'x' : 'Movies Count'},
title = 'Language with Highest Movies : Netflix')
fig.update_layout(plot_bgcolor = 'white')
fig.show()
# hulu_language_movies = language_data_movies[language_data_movies['Hulu'] != 0].sort_values(by = 'Hulu', ascending = False).reset_index()
# hulu_language_movies = hulu_language_movies.drop(['index', 'Netflix', 'Prime Video', 'Disney+', 'Movies Count'], axis = 1)
hulu_language_high_movies = df_language_high_movies.sort_values(by = 'Hulu', ascending = False).reset_index()
hulu_language_high_movies = hulu_language_high_movies.drop(['index'], axis = 1)
hulu_language_low_movies = df_language_high_movies.sort_values(by = 'Hulu', ascending = True).reset_index()
hulu_language_low_movies = hulu_language_low_movies.drop(['index'], axis = 1)
hulu_language_high_movies.head(5)
| Language | Movies Count | Netflix | Hulu | Prime Video | Disney+ | |
|---|---|---|---|---|---|---|
| 0 | English | 13524 | 2370 | 950 | 10146 | 556 |
| 1 | French | 875 | 239 | 102 | 553 | 28 |
| 2 | Spanish | 953 | 356 | 88 | 528 | 23 |
| 3 | German | 526 | 118 | 53 | 361 | 16 |
| 4 | Japanese | 340 | 101 | 49 | 199 | 5 |
fig = px.bar(x = hulu_language_high_movies['Language'][:15],
y = hulu_language_high_movies['Hulu'][:15],
color = hulu_language_high_movies['Hulu'][:15],
color_continuous_scale = 'Teal_r',
labels = { 'y' : 'Language', 'x' : 'Movies Count'},
title = 'Language with Highest Movies : Hulu')
fig.update_layout(plot_bgcolor = 'white')
fig.show()
# prime_video_language_movies = language_data_movies[language_data_movies['Prime Video'] != 0].sort_values(by = 'Prime Video', ascending = False).reset_index()
# prime_video_language_movies = prime_video_language_movies.drop(['index', 'Netflix', 'Hulu', 'Disney+', 'Movies Count'], axis = 1)
prime_video_language_high_movies = df_language_high_movies.sort_values(by = 'Prime Video', ascending = False).reset_index()
prime_video_language_high_movies = prime_video_language_high_movies.drop(['index'], axis = 1)
prime_video_language_low_movies = df_language_high_movies.sort_values(by = 'Prime Video', ascending = True).reset_index()
prime_video_language_low_movies = prime_video_language_low_movies.drop(['index'], axis = 1)
prime_video_language_high_movies.head(5)
| Language | Movies Count | Netflix | Hulu | Prime Video | Disney+ | |
|---|---|---|---|---|---|---|
| 0 | English | 13524 | 2370 | 950 | 10146 | 556 |
| 1 | French | 875 | 239 | 102 | 553 | 28 |
| 2 | Spanish | 953 | 356 | 88 | 528 | 23 |
| 3 | Hindi | 720 | 368 | 6 | 410 | 7 |
| 4 | Italian | 491 | 87 | 32 | 378 | 11 |
fig = px.bar(x = prime_video_language_high_movies['Language'][:15],
y = prime_video_language_high_movies['Prime Video'][:15],
color = prime_video_language_high_movies['Prime Video'][:15],
color_continuous_scale = 'Teal_r',
labels = { 'y' : 'Language', 'x' : 'Movies Count'},
title = 'Language with Highest Movies : Prime Video')
fig.update_layout(plot_bgcolor = 'white')
fig.show()
# disney_language_movies = language_data_movies[language_data_movies['Disney+'] != 0].sort_values(by = 'Disney+', ascending = False).reset_index()
# disney_language_movies = disney_language_movies.drop(['index', 'Netflix', 'Hulu', 'Prime Video', 'Movies Count'], axis = 1)
disney_language_high_movies = df_language_high_movies.sort_values(by = 'Disney+', ascending = False).reset_index()
disney_language_high_movies = disney_language_high_movies.drop(['index'], axis = 1)
disney_language_low_movies = df_language_high_movies.sort_values(by = 'Disney+', ascending = True).reset_index()
disney_language_low_movies = disney_language_low_movies.drop(['index'], axis = 1)
disney_language_high_movies.head(5)
| Language | Movies Count | Netflix | Hulu | Prime Video | Disney+ | |
|---|---|---|---|---|---|---|
| 0 | English | 13524 | 2370 | 950 | 10146 | 556 |
| 1 | French | 875 | 239 | 102 | 553 | 28 |
| 2 | Spanish | 953 | 356 | 88 | 528 | 23 |
| 3 | German | 526 | 118 | 53 | 361 | 16 |
| 4 | Italian | 491 | 87 | 32 | 378 | 11 |
fig = px.bar(x = disney_language_high_movies['Language'][:15],
y = disney_language_high_movies['Disney+'][:15],
color = disney_language_high_movies['Disney+'][:15],
color_continuous_scale = 'Teal_r',
labels = { 'y' : 'Language', 'x' : 'Movies Count'},
title = 'Language with Highest Movies : Disney+')
fig.update_layout(plot_bgcolor = 'white')
fig.show()
f, ax = plt.subplots(1, 2 , figsize = (20, 5))
sns.distplot(language_data_movies['Movies Count'], bins = 20, kde = True, ax = ax[0])
sns.boxplot(language_data_movies['Movies Count'], ax = ax[1])
plt.show()
# Creating distinct dataframes only with the movies present on individual streaming platforms
netflix_language_movies = language_data_movies[language_data_movies['Netflix'] != 0].sort_values(by = 'Netflix', ascending = False).reset_index()
netflix_language_movies = netflix_language_movies.drop(['index', 'Hulu', 'Prime Video', 'Disney+', 'Movies Count'], axis = 1)
hulu_language_movies = language_data_movies[language_data_movies['Hulu'] != 0].sort_values(by = 'Hulu', ascending = False).reset_index()
hulu_language_movies = hulu_language_movies.drop(['index', 'Netflix', 'Prime Video', 'Disney+', 'Movies Count'], axis = 1)
prime_video_language_movies = language_data_movies[language_data_movies['Prime Video'] != 0].sort_values(by = 'Prime Video', ascending = False).reset_index()
prime_video_language_movies = prime_video_language_movies.drop(['index', 'Netflix', 'Hulu', 'Disney+', 'Movies Count'], axis = 1)
disney_language_movies = language_data_movies[language_data_movies['Disney+'] != 0].sort_values(by = 'Disney+', ascending = False).reset_index()
disney_language_movies = disney_language_movies.drop(['index', 'Netflix', 'Hulu', 'Prime Video', 'Movies Count'], axis = 1)
# Defining plot size and title
plt.figure(figsize = (20, 5))
plt.title('Language Movies Count Per Platform')
# Plotting the information from each dataset into a histogram
sns.histplot(disney_language_movies['Disney+'][:50], color = 'darkblue', legend = True, kde = True)
sns.histplot(prime_video_language_movies['Prime Video'][:50], color = 'lightblue', legend = True, kde = True)
sns.histplot(netflix_language_movies['Netflix'][:50], color = 'red', legend = True, kde = True)
sns.histplot(hulu_language_movies['Hulu'][:50], color = 'lightgreen', legend = True, kde = True)
# Setting the legend
plt.legend(['Disney+', 'Prime Video', 'Netflix', 'Hulu'])
plt.show()
print(f'''
The Language with Highest Movies Count Ever Got is '{df_language_high_movies['Language'][0]}' : '{df_language_high_movies['Movies Count'].max()}'\n
The Language with Lowest Movies Count Ever Got is '{df_language_low_movies['Language'][0]}' : '{df_language_low_movies['Movies Count'].min()}'\n
The Language with Highest Movies Count on 'Netflix' is '{netflix_language_high_movies['Language'][0]}' : '{netflix_language_high_movies['Netflix'].max()}'\n
The Language with Lowest Movies Count on 'Netflix' is '{netflix_language_low_movies['Language'][0]}' : '{netflix_language_low_movies['Netflix'].min()}'\n
The Language with Highest Movies Count on 'Hulu' is '{hulu_language_high_movies['Language'][0]}' : '{hulu_language_high_movies['Hulu'].max()}'\n
The Language with Lowest Movies Count on 'Hulu' is '{hulu_language_low_movies['Language'][0]}' : '{hulu_language_low_movies['Hulu'].min()}'\n
The Language with Highest Movies Count on 'Prime Video' is '{prime_video_language_high_movies['Language'][0]}' : '{prime_video_language_high_movies['Prime Video'].max()}'\n
The Language with Lowest Movies Count on 'Prime Video' is '{prime_video_language_low_movies['Language'][0]}' : '{prime_video_language_low_movies['Prime Video'].min()}'\n
The Language with Highest Movies Count on 'Disney+' is '{disney_language_high_movies['Language'][0]}' : '{disney_language_high_movies['Disney+'].max()}'\n
The Language with Lowest Movies Count on 'Disney+' is '{disney_language_low_movies['Language'][0]}' : '{disney_language_low_movies['Disney+'].min()}'\n
''')
The Language with Highest Movies Count Ever Got is 'English' : '13524'
The Language with Lowest Movies Count Ever Got is 'Lao' : '1'
The Language with Highest Movies Count on 'Netflix' is 'English' : '2370'
The Language with Lowest Movies Count on 'Netflix' is 'North American Indian' : '0'
The Language with Highest Movies Count on 'Hulu' is 'English' : '950'
The Language with Lowest Movies Count on 'Hulu' is 'North American Indian' : '0'
The Language with Highest Movies Count on 'Prime Video' is 'English' : '10146'
The Language with Lowest Movies Count on 'Prime Video' is 'Greenlandic' : '0'
The Language with Highest Movies Count on 'Disney+' is 'English' : '556'
The Language with Lowest Movies Count on 'Disney+' is 'Lao' : '0'
# Distribution of movies language in each platform
plt.figure(figsize = (20, 5))
plt.title('Language with Movies Count for All Platforms')
sns.violinplot(x = language_data_movies['Movies Count'][:100], color = 'gold', legend = True, kde = True, shade = False)
plt.show()
# Distribution of Language Movies Count in each platform
f1, ax1 = plt.subplots(1, 2 , figsize = (20, 5))
sns.violinplot(x = netflix_language_movies['Netflix'][:100], color = 'red', ax = ax1[0])
sns.violinplot(x = hulu_language_movies['Hulu'][:100], color = 'lightgreen', ax = ax1[1])
f2, ax2 = plt.subplots(1, 2 , figsize = (20, 5))
sns.violinplot(x = prime_video_language_movies['Prime Video'][:100], color = 'lightblue', ax = ax2[0])
sns.violinplot(x = disney_language_movies['Disney+'][:100], color = 'darkblue', ax = ax2[1])
plt.show()
print(f'''
Accross All Platforms the Average Movies Count of Language is '{round(language_data_movies['Movies Count'].mean(), ndigits = 2)}'\n
The Average Movies Count of Language on 'Netflix' is '{round(netflix_language_movies['Netflix'].mean(), ndigits = 2)}'\n
The Average Movies Count of Language on 'Hulu' is '{round(hulu_language_movies['Hulu'].mean(), ndigits = 2)}'\n
The Average Movies Count of Language on 'Prime Video' is '{round(prime_video_language_movies['Prime Video'].mean(), ndigits = 2)}'\n
The Average Movies Count of Language on 'Disney+' is '{round(disney_language_movies['Disney+'].mean(), ndigits = 2)}'\n
''')
Accross All Platforms the Average Movies Count of Language is '115.41'
The Average Movies Count of Language on 'Netflix' is '40.8'
The Average Movies Count of Language on 'Hulu' is '20.52'
The Average Movies Count of Language on 'Prime Video' is '95.85'
The Average Movies Count of Language on 'Disney+' is '12.81'
print(f'''
Accross All Platforms Total Count of Language is '{language_data_movies['Language'].unique().shape[0]}'\n
Total Count of Language on 'Netflix' is '{netflix_language_movies['Language'].unique().shape[0]}'\n
Total Count of Language on 'Hulu' is '{hulu_language_movies['Language'].unique().shape[0]}'\n
Total Count of Language on 'Prime Video' is '{prime_video_language_movies['Language'].unique().shape[0]}'\n
Total Count of Language on 'Disney+' is '{disney_language_movies['Language'].unique().shape[0]}'\n
''')
Accross All Platforms Total Count of Language is '183'
Total Count of Language on 'Netflix' is '120'
Total Count of Language on 'Hulu' is '75'
Total Count of Language on 'Prime Video' is '155'
Total Count of Language on 'Disney+' is '58'
plt.figure(figsize = (20, 5))
sns.lineplot(x = language_data_movies['Language'][:10], y = language_data_movies['Netflix'][:10], color = 'red')
sns.lineplot(x = language_data_movies['Language'][:10], y = language_data_movies['Hulu'][:10], color = 'lightgreen')
sns.lineplot(x = language_data_movies['Language'][:10], y = language_data_movies['Prime Video'][:10], color = 'lightblue')
sns.lineplot(x = language_data_movies['Language'][:10], y = language_data_movies['Disney+'][:10], color = 'darkblue')
plt.xlabel('Language', fontsize = 20)
plt.ylabel('Movies Count', fontsize = 20)
plt.show()
fig, axes = plt.subplots(2, 2, figsize = (20 , 10))
n_l_ax1 = sns.lineplot(y = language_data_movies['Language'][:10], x = language_data_movies['Netflix'][:10], color = 'red', ax = axes[0, 0])
h_l_ax2 = sns.lineplot(y = language_data_movies['Language'][:10], x = language_data_movies['Hulu'][:10], color = 'lightgreen', ax = axes[0, 1])
p_l_ax3 = sns.lineplot(y = language_data_movies['Language'][:10], x = language_data_movies['Prime Video'][:10], color = 'lightblue', ax = axes[1, 0])
d_l_ax4 = sns.lineplot(y = language_data_movies['Language'][:10], x = language_data_movies['Disney+'][:10], color = 'darkblue', ax = axes[1, 1])
labels = ['Netflix', 'Hulu', 'Prime Video', 'Disney+']
n_l_ax1.title.set_text(labels[0])
h_l_ax2.title.set_text(labels[1])
p_l_ax3.title.set_text(labels[2])
d_l_ax4.title.set_text(labels[3])
plt.show()
fig, axes = plt.subplots(2, 2, figsize = (20 , 20))
n_l_ax1 = sns.barplot(y = netflix_language_movies['Language'][:10], x = netflix_language_movies['Netflix'][:10], palette = 'Reds_r', ax = axes[0, 0])
h_l_ax2 = sns.barplot(y = hulu_language_movies['Language'][:10], x = hulu_language_movies['Hulu'][:10], palette = 'Greens_r', ax = axes[0, 1])
p_l_ax3 = sns.barplot(y = prime_video_language_movies['Language'][:10], x = prime_video_language_movies['Prime Video'][:10], palette = 'Blues_r', ax = axes[1, 0])
d_l_ax4 = sns.barplot(y = disney_language_movies['Language'][:10], x = disney_language_movies['Disney+'][:10], palette = 'BuPu_r', ax = axes[1, 1])
labels = ['Netflix', 'Hulu', 'Prime Video', 'Disney+']
n_l_ax1.title.set_text(labels[0])
h_l_ax2.title.set_text(labels[1])
p_l_ax3.title.set_text(labels[2])
d_l_ax4.title.set_text(labels[3])
plt.show()
# Defining plot size and title
plt.figure(figsize = (20, 5))
plt.title('Language Movies Count Per Platform')
# Plotting the information from each dataset into a histogram
sns.kdeplot(netflix_language_movies['Netflix'][:10], color = 'red', legend = True)
sns.kdeplot(hulu_language_movies['Hulu'][:10], color = 'green', legend = True)
sns.kdeplot(prime_video_language_movies['Prime Video'][:10], color = 'lightblue', legend = True)
sns.kdeplot(disney_language_movies['Disney+'][:10], color = 'darkblue', legend = True)
# Setting the legend
plt.legend(['Netflix', 'Hulu', 'Prime Video', 'Disney+'])
plt.show()
fig, axes = plt.subplots(2, 2, figsize = (20 , 20))
n_l_ax1 = sns.barplot(y = language_data_movies['Language'][:10], x = language_data_movies['Netflix'][:10], palette = 'Reds_r', ax = axes[0, 0])
h_l_ax2 = sns.barplot(y = language_data_movies['Language'][:10], x = language_data_movies['Hulu'][:10], palette = 'Greens_r', ax = axes[0, 1])
p_l_ax3 = sns.barplot(y = language_data_movies['Language'][:10], x = language_data_movies['Prime Video'][:10], palette = 'Blues_r', ax = axes[1, 0])
d_l_ax4 = sns.barplot(y = language_data_movies['Language'][:10], x = language_data_movies['Disney+'][:10], palette = 'BuPu_r', ax = axes[1, 1])
labels = ['Netflix', 'Hulu', 'Prime Video', 'Disney+']
n_l_ax1.title.set_text(labels[0])
h_l_ax2.title.set_text(labels[1])
p_l_ax3.title.set_text(labels[2])
d_l_ax4.title.set_text(labels[3])
plt.show()
df_movies_mixed_languages.drop(df_movies_mixed_languages.loc[df_movies_mixed_languages['Language'] == "NA"].index, inplace = True)
# df_movies_mixed_languages = df_movies_mixed_languages[df_movies_mixed_languages.Language != "NA"]
df_movies_mixed_languages.drop(df_movies_mixed_languages.loc[df_movies_mixed_languages['Number of Languages'] == 1].index, inplace = True)
df_movies_mixed_languages.head(5)
| ID | Title | Year | Age | IMDb | Rotten Tomatoes | Directors | Cast | Genres | Country | ... | Plotline | Runtime | Kind | Netflix | Hulu | Prime Video | Disney+ | Type | Service Provider | Number of Languages | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | Inception | 2010 | 13 | 8.8 | 87 | Christopher Nolan | Leonardo DiCaprio,Joseph Gordon-Levitt,Elliot ... | Action,Adventure,Sci-Fi,Thriller | United States,United Kingdom | ... | Dom Cobb is a skilled thief, the absolute best... | 148 | movie | 1 | 0 | 0 | 0 | 0 | Netflix | 3 |
| 5 | 6 | Spider-Man: Into the Spider-Verse | 2018 | 7 | 8.4 | 97 | Bob Persichetti,Peter Ramsey,Rodney Rothman | Shameik Moore,Jake Johnson,Hailee Steinfeld,Ma... | Animation,Action,Adventure,Family,Sci-Fi | United States | ... | Phil Lord and Christopher Miller, the creative... | 117 | movie | 1 | 0 | 0 | 0 | 0 | Netflix | 2 |
| 6 | 7 | The Pianist | 2002 | 16 | 8.5 | 95 | Roman Polanski | Adrien Brody,Emilia Fox,Michal Zebrowski,Ed St... | Biography,Drama,Music,War | United Kingdom,France,Poland,Germany,United St... | ... | In this adaptation of the autobiography "The P... | 150 | movie | 1 | 0 | 1 | 0 | 0 | Netflix | 3 |
| 7 | 8 | Django Unchained | 2012 | 16 | 8.4 | 87 | Quentin Tarantino | Jamie Foxx,Christoph Waltz,Leonardo DiCaprio,K... | Drama,Western | United States | ... | In 1858, a bounty-hunter named King Schultz se... | 165 | movie | 1 | 0 | 0 | 0 | 0 | Netflix | 4 |
| 8 | 9 | Raiders of the Lost Ark | 1981 | 7 | 8.4 | 95 | Steven Spielberg | Harrison Ford,Karen Allen,Paul Freeman,Ronald ... | Action,Adventure | United States | ... | The year is 1936. An archeology professor name... | 115 | movie | 1 | 0 | 0 | 0 | 0 | Netflix | 6 |
5 rows × 21 columns
mixed_languages_count = df_movies_mixed_languages.groupby('Language')['Title'].count()
mixed_languages_movies = df_movies_mixed_languages.groupby('Language')[['Netflix', 'Hulu', 'Prime Video', 'Disney+']].sum()
mixed_languages_data_movies = pd.concat([mixed_languages_count, mixed_languages_movies], axis = 1).reset_index().rename(columns = {'Title' : 'Movies Count', 'Language' : 'Mixed Language'})
mixed_languages_data_movies = mixed_languages_data_movies.sort_values(by = 'Movies Count', ascending = False)
mixed_languages_data_movies.head(5)
| Mixed Language | Movies Count | Netflix | Hulu | Prime Video | Disney+ | |
|---|---|---|---|---|---|---|
| 510 | English,Spanish | 306 | 68 | 39 | 208 | 13 |
| 181 | English,French | 201 | 50 | 28 | 122 | 17 |
| 244 | English,German | 87 | 16 | 2 | 66 | 8 |
| 334 | English,Italian | 78 | 11 | 6 | 58 | 3 |
| 739 | Hindi,English | 64 | 39 | 1 | 31 | 0 |
# Mixed Language with Movies Counts - All Platforms Combined
mixed_languages_data_movies.sort_values(by = 'Movies Count', ascending = False)[:10]
| Mixed Language | Movies Count | Netflix | Hulu | Prime Video | Disney+ | |
|---|---|---|---|---|---|---|
| 510 | English,Spanish | 306 | 68 | 39 | 208 | 13 |
| 181 | English,French | 201 | 50 | 28 | 122 | 17 |
| 244 | English,German | 87 | 16 | 2 | 66 | 8 |
| 334 | English,Italian | 78 | 11 | 6 | 58 | 3 |
| 739 | Hindi,English | 64 | 39 | 1 | 31 | 0 |
| 986 | Spanish,English | 59 | 24 | 4 | 32 | 0 |
| 626 | French,English | 45 | 7 | 6 | 32 | 0 |
| 355 | English,Japanese | 42 | 13 | 4 | 25 | 1 |
| 468 | English,Russian | 41 | 13 | 6 | 24 | 0 |
| 605 | Filipino,Tagalog | 36 | 22 | 0 | 16 | 0 |
df_mixed_languages_high_movies = mixed_languages_data_movies.sort_values(by = 'Movies Count', ascending = False).reset_index()
df_mixed_languages_high_movies = df_mixed_languages_high_movies.drop(['index'], axis = 1)
# filter = (mixed_languages_data_movies['Movies Count'] = = (mixed_languages_data_movies['Movies Count'].max()))
# df_mixed_languages_high_movies = mixed_languages_data_movies[filter]
# highest_rated_movies = mixed_languages_data_movies.loc[mixed_languages_data_movies['Movies Count'].idxmax()]
print('\nMixed Language with Highest Ever Movies Count are : All Platforms Combined\n')
df_mixed_languages_high_movies.head(5)
Mixed Language with Highest Ever Movies Count are : All Platforms Combined
| Mixed Language | Movies Count | Netflix | Hulu | Prime Video | Disney+ | |
|---|---|---|---|---|---|---|
| 0 | English,Spanish | 306 | 68 | 39 | 208 | 13 |
| 1 | English,French | 201 | 50 | 28 | 122 | 17 |
| 2 | English,German | 87 | 16 | 2 | 66 | 8 |
| 3 | English,Italian | 78 | 11 | 6 | 58 | 3 |
| 4 | Hindi,English | 64 | 39 | 1 | 31 | 0 |
fig = px.bar(y = df_mixed_languages_high_movies['Mixed Language'][:15],
x = df_mixed_languages_high_movies['Movies Count'][:15],
color = df_mixed_languages_high_movies['Movies Count'][:15],
color_continuous_scale = 'Teal_r',
labels = { 'y' : 'Movies', 'x' : 'Number of Mixed Language'},
title = 'Movies with Highest Number of Mixed Languages : All Platforms')
fig.update_layout(plot_bgcolor = 'white')
fig.show()
df_mixed_languages_low_movies = mixed_languages_data_movies.sort_values(by = 'Movies Count', ascending = True).reset_index()
df_mixed_languages_low_movies = df_mixed_languages_low_movies.drop(['index'], axis = 1)
# filter = (mixed_languages_data_movies['Movies Count'] = = (mixed_languages_data_movies['Movies Count'].min()))
# df_mixed_languages_low_movies = mixed_languages_data_movies[filter]
print('\nMixed Language with Lowest Ever Movies Count are : All Platforms Combined\n')
df_mixed_languages_low_movies.head(5)
Mixed Language with Lowest Ever Movies Count are : All Platforms Combined
| Mixed Language | Movies Count | Netflix | Hulu | Prime Video | Disney+ | |
|---|---|---|---|---|---|---|
| 0 | Italian,Hungarian,Latin | 1 | 0 | 0 | 1 | 0 |
| 1 | English,French,Tibetan,Mandarin,Russian,Hindi,... | 1 | 1 | 0 | 0 | 0 |
| 2 | English,French,Turkish,German,Greek,Italian | 1 | 0 | 0 | 1 | 0 |
| 3 | English,French,Ukrainian | 1 | 1 | 0 | 0 | 0 |
| 4 | English,Gallegan | 1 | 0 | 0 | 1 | 0 |
fig = px.bar(y = df_mixed_languages_low_movies['Mixed Language'][:15],
x = df_mixed_languages_low_movies['Movies Count'][:15],
color = df_mixed_languages_low_movies['Movies Count'][:15],
color_continuous_scale = 'Teal_r',
labels = { 'y' : 'Movies', 'x' : 'Number of Mixed Language'},
title = 'Movies with Lowest Number of Mixed Languages : All Platforms')
fig.update_layout(plot_bgcolor = 'white')
fig.show()
print(f'''
Total '{df_movies_languages['Language'].count()}' Titles are available on All Platforms, out of which\n
You Can Choose to see Movies from Total '{mixed_languages_data_movies['Mixed Language'].unique().shape[0]}' Mixed Language, They were Like this, \n
{mixed_languages_data_movies.sort_values(by = 'Movies Count', ascending = False)['Mixed Language'].head(5).unique()} etc. \n
The Mixed Language with Highest Movies Count have '{mixed_languages_data_movies['Movies Count'].max()}' Movies Available is '{df_mixed_languages_high_movies['Mixed Language'][0]}', &\n
The Mixed Language with Lowest Movies Count have '{mixed_languages_data_movies['Movies Count'].min()}' Movies Available is '{df_mixed_languages_low_movies['Mixed Language'][0]}'
''')
Total '16486' Titles are available on All Platforms, out of which
You Can Choose to see Movies from Total '1091' Mixed Language, They were Like this,
['English,Spanish' 'English,French' 'English,German' 'English,Italian'
'Hindi,English'] etc.
The Mixed Language with Highest Movies Count have '306' Movies Available is 'English,Spanish', &
The Mixed Language with Lowest Movies Count have '1' Movies Available is 'Italian,Hungarian,Latin'
fig = px.pie(mixed_languages_data_movies[:10], names = 'Mixed Language', values = 'Movies Count', color_discrete_sequence = px.colors.sequential.Teal)
fig.update_traces(textposition = 'inside', textinfo = 'percent+label', title = 'Movies Count based on Mixed Language')
fig.show()
# netflix_mixed_languages_movies = mixed_languages_data_movies[mixed_languages_data_movies['Netflix'] != 0].sort_values(by = 'Netflix', ascending = False).reset_index()
# netflix_mixed_languages_movies = netflix_mixed_languages_movies.drop(['index', 'Hulu', 'Prime Video', 'Disney+', 'Movies Count'], axis = 1)
netflix_mixed_languages_high_movies = df_mixed_languages_high_movies.sort_values(by = 'Netflix', ascending = False).reset_index()
netflix_mixed_languages_high_movies = netflix_mixed_languages_high_movies.drop(['index'], axis = 1)
netflix_mixed_languages_low_movies = df_mixed_languages_high_movies.sort_values(by = 'Netflix', ascending = True).reset_index()
netflix_mixed_languages_low_movies = netflix_mixed_languages_low_movies.drop(['index'], axis = 1)
netflix_mixed_languages_high_movies.head(5)
| Mixed Language | Movies Count | Netflix | Hulu | Prime Video | Disney+ | |
|---|---|---|---|---|---|---|
| 0 | English,Spanish | 306 | 68 | 39 | 208 | 13 |
| 1 | English,French | 201 | 50 | 28 | 122 | 17 |
| 2 | Hindi,English | 64 | 39 | 1 | 31 | 0 |
| 3 | Spanish,English | 59 | 24 | 4 | 32 | 0 |
| 4 | Filipino,Tagalog | 36 | 22 | 0 | 16 | 0 |
# hulu_mixed_languages_movies = mixed_languages_data_movies[mixed_languages_data_movies['Hulu'] != 0].sort_values(by = 'Hulu', ascending = False).reset_index()
# hulu_mixed_languages_movies = hulu_mixed_languages_movies.drop(['index', 'Netflix', 'Prime Video', 'Disney+', 'Movies Count'], axis = 1)
hulu_mixed_languages_high_movies = df_mixed_languages_high_movies.sort_values(by = 'Hulu', ascending = False).reset_index()
hulu_mixed_languages_high_movies = hulu_mixed_languages_high_movies.drop(['index'], axis = 1)
hulu_mixed_languages_low_movies = df_mixed_languages_high_movies.sort_values(by = 'Hulu', ascending = True).reset_index()
hulu_mixed_languages_low_movies = hulu_mixed_languages_low_movies.drop(['index'], axis = 1)
hulu_mixed_languages_high_movies.head(5)
| Mixed Language | Movies Count | Netflix | Hulu | Prime Video | Disney+ | |
|---|---|---|---|---|---|---|
| 0 | English,Spanish | 306 | 68 | 39 | 208 | 13 |
| 1 | English,French | 201 | 50 | 28 | 122 | 17 |
| 2 | English,Mandarin | 36 | 12 | 8 | 20 | 3 |
| 3 | English,Italian | 78 | 11 | 6 | 58 | 3 |
| 4 | French,English | 45 | 7 | 6 | 32 | 0 |
# prime_video_mixed_languages_movies = mixed_languages_data_movies[mixed_languages_data_movies['Prime Video'] != 0].sort_values(by = 'Prime Video', ascending = False).reset_index()
# prime_video_mixed_languages_movies = prime_video_mixed_languages_movies.drop(['index', 'Netflix', 'Hulu', 'Disney+', 'Movies Count'], axis = 1)
prime_video_mixed_languages_high_movies = df_mixed_languages_high_movies.sort_values(by = 'Prime Video', ascending = False).reset_index()
prime_video_mixed_languages_high_movies = prime_video_mixed_languages_high_movies.drop(['index'], axis = 1)
prime_video_mixed_languages_low_movies = df_mixed_languages_high_movies.sort_values(by = 'Prime Video', ascending = True).reset_index()
prime_video_mixed_languages_low_movies = prime_video_mixed_languages_low_movies.drop(['index'], axis = 1)
prime_video_mixed_languages_high_movies.head(5)
| Mixed Language | Movies Count | Netflix | Hulu | Prime Video | Disney+ | |
|---|---|---|---|---|---|---|
| 0 | English,Spanish | 306 | 68 | 39 | 208 | 13 |
| 1 | English,French | 201 | 50 | 28 | 122 | 17 |
| 2 | English,German | 87 | 16 | 2 | 66 | 8 |
| 3 | English,Italian | 78 | 11 | 6 | 58 | 3 |
| 4 | Spanish,English | 59 | 24 | 4 | 32 | 0 |
# disney_mixed_languages_movies = mixed_languages_data_movies[mixed_languages_data_movies['Disney+'] != 0].sort_values(by = 'Disney+', ascending = False).reset_index()
# disney_mixed_languages_movies = disney_mixed_languages_movies.drop(['index', 'Netflix', 'Hulu', 'Prime Video', 'Movies Count'], axis = 1)
disney_mixed_languages_high_movies = df_mixed_languages_high_movies.sort_values(by = 'Disney+', ascending = False).reset_index()
disney_mixed_languages_high_movies = disney_mixed_languages_high_movies.drop(['index'], axis = 1)
disney_mixed_languages_low_movies = df_mixed_languages_high_movies.sort_values(by = 'Disney+', ascending = True).reset_index()
disney_mixed_languages_low_movies = disney_mixed_languages_low_movies.drop(['index'], axis = 1)
disney_mixed_languages_high_movies.head(5)
| Mixed Language | Movies Count | Netflix | Hulu | Prime Video | Disney+ | |
|---|---|---|---|---|---|---|
| 0 | English,French | 201 | 50 | 28 | 122 | 17 |
| 1 | English,Spanish | 306 | 68 | 39 | 208 | 13 |
| 2 | English,German | 87 | 16 | 2 | 66 | 8 |
| 3 | English,Portuguese | 19 | 2 | 3 | 14 | 4 |
| 4 | English,Italian | 78 | 11 | 6 | 58 | 3 |
f, ax = plt.subplots(1, 2 , figsize = (20, 5))
sns.distplot(mixed_languages_data_movies['Movies Count'], bins = 20, kde = True, ax = ax[0])
sns.boxplot(mixed_languages_data_movies['Movies Count'], ax = ax[1])
plt.show()
# Creating distinct dataframes only with the movies present on individual streaming platforms
netflix_mixed_languages_movies = mixed_languages_data_movies[mixed_languages_data_movies['Netflix'] != 0].sort_values(by = 'Netflix', ascending = False).reset_index()
netflix_mixed_languages_movies = netflix_mixed_languages_movies.drop(['index', 'Hulu', 'Prime Video', 'Disney+', 'Movies Count'], axis = 1)
hulu_mixed_languages_movies = mixed_languages_data_movies[mixed_languages_data_movies['Hulu'] != 0].sort_values(by = 'Hulu', ascending = False).reset_index()
hulu_mixed_languages_movies = hulu_mixed_languages_movies.drop(['index', 'Netflix', 'Prime Video', 'Disney+', 'Movies Count'], axis = 1)
prime_video_mixed_languages_movies = mixed_languages_data_movies[mixed_languages_data_movies['Prime Video'] != 0].sort_values(by = 'Prime Video', ascending = False).reset_index()
prime_video_mixed_languages_movies = prime_video_mixed_languages_movies.drop(['index', 'Netflix', 'Hulu', 'Disney+', 'Movies Count'], axis = 1)
disney_mixed_languages_movies = mixed_languages_data_movies[mixed_languages_data_movies['Disney+'] != 0].sort_values(by = 'Disney+', ascending = False).reset_index()
disney_mixed_languages_movies = disney_mixed_languages_movies.drop(['index', 'Netflix', 'Hulu', 'Prime Video', 'Movies Count'], axis = 1)
# Defining plot size and title
plt.figure(figsize = (20, 5))
plt.title('Mixed Language Movies Count Per Platform')
# Plotting the information from each dataset into a histogram
sns.histplot(prime_video_mixed_languages_movies['Prime Video'][:100], color = 'lightblue', legend = True, kde = True)
sns.histplot(netflix_mixed_languages_movies['Netflix'][:100], color = 'red', legend = True, kde = True)
sns.histplot(hulu_mixed_languages_movies['Hulu'][:100], color = 'lightgreen', legend = True, kde = True)
sns.histplot(disney_mixed_languages_movies['Disney+'][:100], color = 'darkblue', legend = True, kde = True)
# Setting the legend
plt.legend(['Prime Video', 'Netflix', 'Hulu', 'Disney+'])
plt.show()
print(f'''
The Mixed Language with Highest Movies Count Ever Got is '{df_mixed_languages_high_movies['Mixed Language'][0]}' : '{df_mixed_languages_high_movies['Movies Count'].max()}'\n
The Mixed Language with Lowest Movies Count Ever Got is '{df_mixed_languages_low_movies['Mixed Language'][0]}' : '{df_mixed_languages_low_movies['Movies Count'].min()}'\n
The Mixed Language with Highest Movies Count on 'Netflix' is '{netflix_mixed_languages_high_movies['Mixed Language'][0]}' : '{netflix_mixed_languages_high_movies['Netflix'].max()}'\n
The Mixed Language with Lowest Movies Count on 'Netflix' is '{netflix_mixed_languages_low_movies['Mixed Language'][0]}' : '{netflix_mixed_languages_low_movies['Netflix'].min()}'\n
The Mixed Language with Highest Movies Count on 'Hulu' is '{hulu_mixed_languages_high_movies['Mixed Language'][0]}' : '{hulu_mixed_languages_high_movies['Hulu'].max()}'\n
The Mixed Language with Lowest Movies Count on 'Hulu' is '{hulu_mixed_languages_low_movies['Mixed Language'][0]}' : '{hulu_mixed_languages_low_movies['Hulu'].min()}'\n
The Mixed Language with Highest Movies Count on 'Prime Video' is '{prime_video_mixed_languages_high_movies['Mixed Language'][0]}' : '{prime_video_mixed_languages_high_movies['Prime Video'].max()}'\n
The Mixed Language with Lowest Movies Count on 'Prime Video' is '{prime_video_mixed_languages_low_movies['Mixed Language'][0]}' : '{prime_video_mixed_languages_low_movies['Prime Video'].min()}'\n
The Mixed Language with Highest Movies Count on 'Disney+' is '{disney_mixed_languages_high_movies['Mixed Language'][0]}' : '{disney_mixed_languages_high_movies['Disney+'].max()}'\n
The Mixed Language with Lowest Movies Count on 'Disney+' is '{disney_mixed_languages_low_movies['Mixed Language'][0]}' : '{disney_mixed_languages_low_movies['Disney+'].min()}'\n
''')
The Mixed Language with Highest Movies Count Ever Got is 'English,Spanish' : '306'
The Mixed Language with Lowest Movies Count Ever Got is 'Italian,Hungarian,Latin' : '1'
The Mixed Language with Highest Movies Count on 'Netflix' is 'English,Spanish' : '68'
The Mixed Language with Lowest Movies Count on 'Netflix' is 'English,Polish,Cantonese,Italian' : '0'
The Mixed Language with Highest Movies Count on 'Hulu' is 'English,Spanish' : '39'
The Mixed Language with Lowest Movies Count on 'Hulu' is 'English,Polish,Cantonese,Italian' : '0'
The Mixed Language with Highest Movies Count on 'Prime Video' is 'English,Spanish' : '208'
The Mixed Language with Lowest Movies Count on 'Prime Video' is 'Yoruba,Ibo,English' : '0'
The Mixed Language with Highest Movies Count on 'Disney+' is 'English,French' : '17'
The Mixed Language with Lowest Movies Count on 'Disney+' is 'English,Polish,Cantonese,Italian' : '0'
print(f'''
Accross All Platforms the Average Movies Count of Mixed Language is '{round(mixed_languages_data_movies['Movies Count'].mean(), ndigits = 2)}'\n
The Average Movies Count of Mixed Language on 'Netflix' is '{round(netflix_mixed_languages_movies['Netflix'].mean(), ndigits = 2)}'\n
The Average Movies Count of Mixed Language on 'Hulu' is '{round(hulu_mixed_languages_movies['Hulu'].mean(), ndigits = 2)}'\n
The Average Movies Count of Mixed Language on 'Prime Video' is '{round(prime_video_mixed_languages_movies['Prime Video'].mean(), ndigits = 2)}'\n
The Average Movies Count of Mixed Language on 'Disney+' is '{round(disney_mixed_languages_movies['Disney+'].mean(), ndigits = 2)}'\n
''')
Accross All Platforms the Average Movies Count of Mixed Language is '2.76'
The Average Movies Count of Mixed Language on 'Netflix' is '2.06'
The Average Movies Count of Mixed Language on 'Hulu' is '1.82'
The Average Movies Count of Mixed Language on 'Prime Video' is '2.57'
The Average Movies Count of Mixed Language on 'Disney+' is '1.89'
print(f'''
Accross All Platforms Total Count of Mixed Language is '{mixed_languages_data_movies['Mixed Language'].unique().shape[0]}'\n
Total Count of Mixed Language on 'Netflix' is '{netflix_mixed_languages_movies['Mixed Language'].unique().shape[0]}'\n
Total Count of Mixed Language on 'Hulu' is '{hulu_mixed_languages_movies['Mixed Language'].unique().shape[0]}'\n
Total Count of Mixed Language on 'Prime Video' is '{prime_video_mixed_languages_movies['Mixed Language'].unique().shape[0]}'\n
Total Count of Mixed Language on 'Disney+' is '{disney_mixed_languages_movies['Mixed Language'].unique().shape[0]}'\n
''')
Accross All Platforms Total Count of Mixed Language is '1091'
Total Count of Mixed Language on 'Netflix' is '417'
Total Count of Mixed Language on 'Hulu' is '157'
Total Count of Mixed Language on 'Prime Video' is '751'
Total Count of Mixed Language on 'Disney+' is '57'
plt.figure(figsize = (20, 5))
sns.lineplot(x = mixed_languages_data_movies['Mixed Language'][:5], y = mixed_languages_data_movies['Netflix'][:5], color = 'red')
sns.lineplot(x = mixed_languages_data_movies['Mixed Language'][:5], y = mixed_languages_data_movies['Hulu'][:5], color = 'lightgreen')
sns.lineplot(x = mixed_languages_data_movies['Mixed Language'][:5], y = mixed_languages_data_movies['Prime Video'][:5], color = 'lightblue')
sns.lineplot(x = mixed_languages_data_movies['Mixed Language'][:5], y = mixed_languages_data_movies['Disney+'][:5], color = 'darkblue')
plt.xlabel('Mixed Language', fontsize = 15)
plt.ylabel('Movies Count', fontsize = 15)
plt.show()
fig, axes = plt.subplots(2, 2, figsize = (20 , 20))
n_l_ax1 = sns.barplot(y = mixed_languages_data_movies['Mixed Language'][:10], x = mixed_languages_data_movies['Netflix'][:10], palette = 'Reds_r', ax = axes[0, 0])
h_l_ax2 = sns.barplot(y = mixed_languages_data_movies['Mixed Language'][:10], x = mixed_languages_data_movies['Hulu'][:10], palette = 'Greens_r', ax = axes[0, 1])
p_l_ax3 = sns.barplot(y = mixed_languages_data_movies['Mixed Language'][:10], x = mixed_languages_data_movies['Prime Video'][:10], palette = 'Blues_r', ax = axes[1, 0])
d_l_ax4 = sns.barplot(y = mixed_languages_data_movies['Mixed Language'][:10], x = mixed_languages_data_movies['Disney+'][:10], palette = 'BuPu_r', ax = axes[1, 1])
labels = ['Netflix', 'Hulu', 'Prime Video', 'Disney+']
n_l_ax1.title.set_text(labels[0])
h_l_ax2.title.set_text(labels[1])
p_l_ax3.title.set_text(labels[2])
d_l_ax4.title.set_text(labels[3])
plt.show()
fig, axes = plt.subplots(2, 2, figsize = (20 , 10))
n_ml_ax1 = sns.lineplot(y = mixed_languages_data_movies['Mixed Language'][:10], x = mixed_languages_data_movies['Netflix'][:10], color = 'red', ax = axes[0, 0])
h_ml_ax2 = sns.lineplot(y = mixed_languages_data_movies['Mixed Language'][:10], x = mixed_languages_data_movies['Hulu'][:10], color = 'lightgreen', ax = axes[0, 1])
p_ml_ax3 = sns.lineplot(y = mixed_languages_data_movies['Mixed Language'][:10], x = mixed_languages_data_movies['Prime Video'][:10], color = 'lightblue', ax = axes[1, 0])
d_ml_ax4 = sns.lineplot(y = mixed_languages_data_movies['Mixed Language'][:10], x = mixed_languages_data_movies['Disney+'][:10], color = 'darkblue', ax = axes[1, 1])
labels = ['Netflix', 'Hulu', 'Prime Video', 'Disney+']
n_ml_ax1.title.set_text(labels[0])
h_ml_ax2.title.set_text(labels[1])
p_ml_ax3.title.set_text(labels[2])
d_ml_ax4.title.set_text(labels[3])
plt.show()
# Defining plot size and title
plt.figure(figsize = (20, 5))
plt.title('Mixed Language Movies Count Per Platform')
# Plotting the information from each dataset into a histogram
sns.kdeplot(netflix_mixed_languages_movies['Netflix'][:50], color = 'red', legend = True)
sns.kdeplot(hulu_mixed_languages_movies['Hulu'][:50], color = 'green', legend = True)
sns.kdeplot(prime_video_mixed_languages_movies['Prime Video'][:50], color = 'lightblue', legend = True)
sns.kdeplot(disney_mixed_languages_movies['Disney+'][:50], color = 'darkblue', legend = True)
# Setting the legend
plt.legend(['Netflix', 'Hulu', 'Prime Video', 'Disney+'])
plt.show()
fig, axes = plt.subplots(2, 2, figsize = (20 , 20))
n_ml_ax1 = sns.barplot(y = netflix_mixed_languages_movies['Mixed Language'][:10], x = netflix_mixed_languages_movies['Netflix'][:10], palette = 'Reds_r', ax = axes[0, 0])
h_ml_ax2 = sns.barplot(y = hulu_mixed_languages_movies['Mixed Language'][:10], x = hulu_mixed_languages_movies['Hulu'][:10], palette = 'Greens_r', ax = axes[0, 1])
p_ml_ax3 = sns.barplot(y = prime_video_mixed_languages_movies['Mixed Language'][:10], x = prime_video_mixed_languages_movies['Prime Video'][:10], palette = 'Blues_r', ax = axes[1, 0])
d_ml_ax4 = sns.barplot(y = disney_mixed_languages_movies['Mixed Language'][:10], x = disney_mixed_languages_movies['Disney+'][:10], palette = 'BuPu_r', ax = axes[1, 1])
labels = ['Netflix', 'Hulu', 'Prime Video', 'Disney+']
n_ml_ax1.title.set_text(labels[0])
h_ml_ax2.title.set_text(labels[1])
p_ml_ax3.title.set_text(labels[2])
d_ml_ax4.title.set_text(labels[3])
plt.show()
fig = go.Figure(go.Funnel(y = mixed_languages_data_movies['Mixed Language'][:10], x = mixed_languages_data_movies['Movies Count'][:10]))
fig.show()